Protein language models enable prediction of polyreactivity of monospecific, bispecific, and heavy-chain-only antibodies.

ABSTRACT:

Background

Early assessment of antibody off-target binding is essential for mitigating developability risks such as fast clearance, reduced efficacy, toxicity, and immunogenicity. The baculovirus particle (BVP) binding assay has been widely utilized to evaluate polyreactivity of antibodies. As a complementary approach, computational prediction of polyreactivity is desirable for counter-screening antibodies from in silico discovery campaigns. However, there is a lack of such models.

Methods

Herein, we present the development of an ensemble of three deep learning models based on two pan-protein foundational protein language models (ESM2 and ProtT5) and an antibody-specific protein language model (PLM) (Antiberty). These models were trained in a transfer learning network

SUBMITTER: Yu X 

PROVIDER: S-EPMC11259759 | biostudies-literature | 2024 Jul

REPOSITORIES: biostudies-literature